In this assignment, assume that you work in an automobile industry to examine different makes and models of automobiles. Your company is particularly interested in clustering of the variables so that they can determine if the data are suitable for use in the next phase of their upcoming analytics project. Complete the following tasks:

1. Locate the data from Libraries My Libraries SASHELP CARS.
2. Using correlations and scatterplots, examine the linear relationships among the quantitative variables: MSRP, Invoice, EngineSize, Cylinder, Horsepower, MPG_City, MPG_Highway, Weight, Wheelbase, and Length. Comment on the relationships.
3. Provide the summary statistics for all quantitative variables using Origin as a classification variable. Comment on the summary statistics. What are some of the key characteristics from the data?
4. Provide a graphical summary (such as histogram) for all quantitative variables using Origin as a classification variable. Comment on the graphs.
5. Using the whole dataset based on quantitative variables, conduct the cluster analysis using either PROC FASTCLUS or PROC VARCLUS or both. Make sure to interpret the SAS output.
6. Repeat part 5 using the variable Origin as a classification (group) variable.

Note: PROC FASTCLUS is based on k-means procedure. More information including description, syntax, and examples can be found from the website: SAS FASTCLUS(Links to an external site.)

PROC VARCLUS is based on linear combinations of the variables in the cluster. More information including description, syntax, and examples can be found from the website: SAS VARCLUS(Links to an external site.)

For each part, take the screenshots of relevant SAS output and paste them into a Word document. Include all relevant calculations and your answers to all assignment items and submit the document to Canvas for grading. Clearly label all elements in your submission.

https://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm#statug_fastclus_sect001.htm